Acoustic noise results from background sounds which interfere with speech sounds to be transmitted. For example, in a cellular mobile telephone environment, acoustic noise may result from background traffic sounds and other road sounds.
The reduction of acoustic noise is important for off-line applications such as the enhancement of previously recorded noisy speech. The reduction of acoustic noise is also important for on-line (i.e. real time) applications such as public telephones, mobile phones, or voice communications in aircraft cockpits. In these situations acoustic noise is extremely undesirable.
The reduction of acoustic noise is important in applications where low bit rate speech coding algorithms are utilized. In many cases, a low bit rate speech coding algorithm stems from a model for a speech signal which is based on the physics and physiology of speech production. Because of reliance on such a model for a speech signal, the performance of a speech coding algorithm can be expected to degrade with respect to quality and intelligibility when the speech signal is degraded by acoustic noise.
For this reason, the reduction of acoustic noise is especially important for a cellular mobile telephone system. The design capacity of the cellular mobile telephone system is soon to be filled in many metropolitan areas. A possible solution to increase the system capacity is to convert the current analog voice channel into a digital channel. Such a digital mobile telephone system should provide all potential users with satisfactory service for another decade. In a typical proposed digital mobile telephone system, the bandwidth allocated for each digital voice channel is 15 kHz, corresponding to a digital data rate of 12 kbps. However, the low bit rate coding algorithms which would be utilized in such a mobile telephone system do not work properly under low signal-to-noise ratio conditions.
Two major approaches have previously been utilized to reduce acoustic noise for a speech signal. The first approach is based on the adaptive LMS (least mean square) noise cancellation algorithm (see, e.g., B. Widrow, et al, "Adaptive Noise Cancelling: Principles and Application," Proc. of IEEE, Vol. 63, No. 12, pp. 1692-1716, December, 1975; G. S. Kang and L. J. Fransen, "Experimentation with an Adaptive Noise-Cancellation Filter," IEEE Trans Circuits and Systems, Vol. CAS-34, No. 7, pp. 753-758, July 1987; D. O'Shaughnessy, "Enhancing Speech Degraded by Additive Noise or Interfering Speakers", IEEE Communications Magazine, February 1989, pp. 46-51). The second approach involves a speech model (see, e.g., J. S. Lim and A. V. Oppenheim, "All-Pole Modeling of Degraded Speech," IEEE Trans. Acous., Speech, and Signal Process., Vol. ASSP-26, No. 3, pp. 197-210, June 1978; J. S. Lim and A. V. Oppenheim, "Enhancement and Bandwidth Compression of Noisy Speech," Proc. IEEE, Vol. 67, No. 12, December 1979, pp. 1586-1604).
The adaptive LMS noise cancellation technique has proven to be very successful in many applications such as notch filtering, periodic interference cancellation, and antenna sidelobe interference cancellation.
The adaptive LMS noise cancellation technique can be applied to acoustic noise cancellation in a speech signal as follows. An acoustic speech signal y is transmitted over a channel to a first microphone that also receives an acoustic noise signal n.sub.o uncorrelated with the signal y. The combined speech signal and noise y+n.sub.o form a primary input for an adaptive LMS noise canceller. A second microphone receives an acoustic noise n.sub.1 correlated with the signal y but correlated in some unknown way with the noise n.sub.o. This second microphone provides a reference input for the LMS noise canceller.
In the LMS noise canceller, adaptive filtering is used to process n.sub.1 to produce an estimated output noise signal n.sub.0 which is as close as possible to the actual noise signal n.sub.o. The signal n.sub.o is subtracted from y+n.sub.o to produce an enhanced speech output signal y+n.sub.o -n.sub.o. In a typical application, the characteristics of the channels used to transmit the primary and reference acoustic signals to the primary and reference microphones are not entirely known and are time varying. Accordingly, in the LMS adaptive noise canceller, the error signal y+n.sub.o -n.sub.o is used to adaptively adjust the filter coefficients in accordance with an LMS algorithm.
The LM noise cancellation technique does not work properly when there are multiple acoustic noise sources located at different locations or when there is a single noise source with a few reflected images. This result is understandable because the best the adaptive LMS noise cancellation technique can do is identify the differential acoustic transfer function of the speech source to the speech microphone and the reference noise source to the speech microphone. Since only one such transfer function can be estimated by the LMS algorithm, multiple acoustic noise sources cannot be treated using the basic LMS algorithm.
The other approach identified above for the reduction of acoustic noise in a speech signal is based on an all-pole vocal tract model. The all-pole vocal tract model for a speech signal utilizes the basic linear prediction principle. The idea is that a speech sample y(k) can be approximated as a linear combination of the past p speech samples plus an error sample, i.e. EQU y(k)=.SIGMA.a.sub.i (y-i)+Gu(k) (1)
Illustratively, to eliminate acoustic noise, the model parameters a.sub.i are first estimated using an autocorrelation method as if there is no noise present. Then, the same noisy speech signal is filtered with a non-causal Wiener filter constructed according to the estimated model parameters. This parameter estimation and noisy speech filtering process is repeated several times until a near optimum performance is achieved. This algorithm is effective and can be carried out off-line on a computer or on-line using specially designed hardware. However, in comparison to the conventional LMS noise canceller described above, this technique is far more complicated and is difficult to implement in hardware for on-line applications.
Accordingly, it is an object of the present invention to provide a noise cancellation filtering technique which is suitable for filtering speech signals to remove acoustic noise. More particularly, it is an object of the present invention to provide a noise reduction filtering technique which has the simplicity and speed of the conventional LMS noise reduction scheme for on-line applications, but which has a greater effectiveness such as the filtering technique based on the all-pole vocal tract model described above.